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-    <h2>What should </h2>
+    <h2> Metabolic Modelling and Simulation Software </h2>
     <hr>
+    <h3> Why are computational models important for metabolic engineering? </h3>
+    <p> Metabolism is a highly complex network of thousands of reactions, and it’s impossible to take into account the impact of each gene, metabolite, enzyme, or even culture media without a mathematical model.
+
+      In order to investigate our strains of interest (T-B18 and C1-G-S-EVO P. Putida), we constructed genome-scale-metabolic models (GSMM) for those chassis. They are generated from an annotated genome by expressing the metabolic network of an organism as a list of mass balanced reactions, which is represented as a so-called stoichiometric matrix. They also relate genes with the proteins they code for and the reactions those proteins are associated with. They helped satisfy two main purposes: describe the metabolism - specifically, a steady-state approximation thereof -  and predict the state of a metabolic network in different scenarios. 
+       </p>
+    <h3> How do we use metabolic models? </h3>
+    <p>
+      There are many mathematical analyses that can be performed on GSMMs; the method we are using is called flux-balance analysis (FBA). 
+    </p>
+    <h3> Setting flux constraints </h3>
+    <p>
+      For each reaction, the range of allowable fluxes is decided based on biological data, the simulation target, and heuristics. A steady-state assumption is also made that couples the reaction fluxes based on the topology and stoichiometry of the GSMM network.
+    </p>
+    <h3> Conducting Flux-Balance Analysis </h3>
+    <p>
+      Flux-balance analysis (FBA) assigns each reaction a flux such that (A) the flux constraints are obeyed and (B) a given objective function is optimized (often set to be a predictor of cellular growth). We aim to use FBA and its associated methods for the in silico discovery of genes that can be added, overexpressed, or knocked out to improve methanol-dependent growth. 
+    </p>
+    <h3> Insights from Flux-Balance Analysis </h3>
+    <p>
+      FDA generates insights on:
+    </p>
     <ul>
-      <li>A clear and concise description of your project.</li>
-      <li>A detailed explanation of why your team chose to work on this particular project.</li>
-      <li>References and sources to document your research.</li>
-      <li>Use illustrations and other visual resources to explain your project.</li>
+      <li>Knockouts</li>
+      <li>Overexpression</li>
+      <li>Concentration of Growth Media and Supplements Required</li>
     </ul>
+    <h3> Knockouts </h3>
+    <p>
+      Gene knockout simulations are performed by systematically removing each gene from the model and re-optimizing the flux distribution using FBA. This involves setting the fluxes associated with the reactions catalyzed by the knockout gene to zero or adjusting them to account for the gene's absence. The impact of each gene knockout on the objective function is assessed by comparing the predicted flux distribution and the optimized objective function value with those of the wild-type (unmodified) model. Genes whose knockout results in a significant decrease in the objective function are considered potential targets for further experimental validation.
+    </p>
+    <h3> Overexpression </h3>
+    <p>
+      Overexpression simulations are performed with maximizing the production of a target metabolite in mind. From the dry-lab perspective, we identified potential overexpression targets using a framework called FSEOF, and are trying to simulate overexpression in a constraint-based modeling python package called COBRApy with an additional imported metabolic network design package called StrainDesign. In order to simulate overexpression in our T-B18 model, we plan to follow the procedure outlined in slides 25-27 of this powerpoint https://cnls.lanl.gov/external/qbio2018/Slides/FBA%202/qBio-FBA-lab-slides.pdf, where we obtain the range of flux values (that exist in the solution space) for reactions that we want to overexpress. Afterwards, we set the ‘lower-bound’ attribute of a reaction to be the maximum value (in the range) from the FVA analysis. The upper-bound can be set to some arbitrarily large value (1000+). By doing this, we essentially constrain the model to high fluxes for reactions of interest that we intend to overexpress. 
+    </p>
+    <h3> Concentration of Growth Media and Supplements Required </h3>
+    <p>
+      From the simulation perspective, we define the growth medium as the fluxes of exchange reactions between the bacteria and the environment. We can add reactions, limit fluxes, and eliminate reactions to simulate different growth media conditions. 
+    </p>
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